Taleb has long made a career on savaging risk modeling; after the 2008 financial crash this gamble paid off handsomely. Just one year before he had published his magnum opus, a lengthy, obsessive, idiosyncratic volume of theories, personal anecdotes, and vicious attacks on various individuals and institutions that had crossed him. The main idea: we are fools if we think we can predict future trends. A stock trader by training rather than an academic, Taleb proudly considers himself a practitioner, and makes his disgust of academics abundantly apparent. He has a theoretical justification: academics are only concerned with producing hermetic models of how the world works, and not with actually testing their theories. Because they are insulated from the effects of bad modeling, they have no incentive to get it right. Taleb calls this tendency “Platonicity,” or the assumption that the world works like our abstract models of it. Scientists, mathematicians, and “risk experts” are the worst offenders, but Taleb finds traces of this fallacy just about everywhere. Theorists draw his ire not simply because they are “almost always wrong,” but because they don’t admit or realize that they are always wrong, and thus lead us into a false complacency, making us highly vulnerable to sudden events that surprise our expectations.

He dubs these highly improbable events “Black Swans.” They are events that don’t fit trends, that occur suddenly without warning; they are essentially unforeseeable. These happen all the time. In fact, Taleb claims that all interesting events, all of the events that really produce significant change, are Black Swans. History is essentially a succession of Black Swans. This is why theorization, projection, and modeling are so dangerous: they cannot take into account Black Swans, and thus draw pictures of the world that are Black Swan free (what Taleb calls “Mediocristan”). Because we actually live in a world dominated by Black Swans (“Extremistan”), all such modeling and projection makes us dangerously blind to reality. Taleb’s answer is direct and uncompromising: can the “experts.” No form of modeling or prediction is helpful, and every form is potentially harmful.

Taleb reserves particularly crushing critiques for the bell curve and business experts. The bell, or “normal,” curve is a mathematical abstraction of population characteristics that is, according to him, entirely arbitrary. Standard deviations are fictional concepts that do not correspond to reality except in artificial populations. The chief danger of bell curves becomes clear when we consider them in relation to effects, or computed outcomes. Extreme outliers in a bell curve have negligible effects on averages. So, for instance, one very rich individual will hardly budge the average distribution of wealth. This has a leveling effect. Ultimately, Taleb’s objection is that outliers are given short shrift. We are accustomed to looking at the world in terms of averages, and assume that distributions cluster around those averages. In actuality, the world works based on extremes (Extremistan): not only are the outliers important, they are all that is important! Note, then, that Taleb’s argument is philosophical as much as mathematic: modeling causes us to think about averages and disregard extreme possibilities and individuals. We would be better off if we started looking at the extremes instead. Taleb’s argument, then, is directly in line with Neoliberal free market ideology, which sees the individual as the primary unit of measurement. The “social” is seen as an abstraction, glossing over the individuals (especially entrepreneurs) that really move the economy.

Logically, the error that Taleb is highlighting is the inductive fallacy: the past cannot actually tell us anything about future events. Because probability distributions are always based solely on past data, they blind us to the possibility of future discontinuities. And yet, discontinuities happen all the time. Taleb places particular emphasis on scaling: this is what linear models get wrong. They plot all possibilities on a linear continuum and forget that single events (and individuals) can snowball to extreme proportions, changing the whole system. Normal modeling makes this seem impossible. Taleb claims that we need to overhaul our mathematics with Mandelbrotian fractals and power laws, which allow for easy scaling of effects. For instance, wealth accumulates quite quickly at certain sites, and can quickly throw a system out of equilibrium. Conversely, when one bank goes under, they all go under. We don’t realize how fragile our systems are. This is exactly what happened in 2008, elevating Taleb to the status of prophet.

Taleb’s response, besides gloating in his recently acquired prominence (in a followup essay written for the second edition in 2010) is to recommend that we emphasize redundancy and flexibility in our global financial systems, to make them less fragile. We need to reduce complexity in our real systems while throwing away our simplifying models. (However, as I’ve noted, Taleb’s main objection to these models is not that they are too simple, but that they obscure randomness and undervalue the impact of discontinuous events.) At the same time, Black Swans can also be positive. These are the unforseen events that generate widescale change for the better. Those who are undeceived and ready can maximize their exposure to positive Black Swans (while minimizing their exposure to negatives ones) and reap disproportionate rewards when they strike. This is the domain of the entrepreneur and the venture capitalist, which is why members of those communities have enthusiastically embraced Taleb’s ideas.